A road damage detection method and system based on machine vision

Abstract

The present invention discloses a pavement damage detection method and system based on machine vision. The method comprises: obtaining a target detection pavement photo corresponding to a target detection pavement area; determining whether a target suspected damaged area exists on the target detection pavement photo; if so, obtaining a target detection pavement type of the target detection pavement area and a target suspected pavement damage type of the target suspected damaged area; comparing the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detection pavement photo based on the target detection pavement type and the target suspected pavement damage type, and determining whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold; if so, determining the target suspected damaged area as a target pavement damage area, and further determining the target detection pavement area as a pavement damage area. The present invention can accurately detect damaged areas on the pavement and accurately locate them.

Description

Technical Field

[0001] The present invention relates to the technical field of road pavement detection, and in particular to a machine vision-based road damage detection method and system. Background Art

[0002] A machine vision system uses a machine vision product (i.e., an image capture device) to convert captured objects into image signals. This signal is then transmitted to a dedicated image processing system, which obtains the object's morphological information. This information is then converted into digital signals based on pixel distribution, brightness, color, and other information. The image system then performs various operations on these signals to extract the target's features and, based on the resulting information, controls the operation of on-site equipment. With the advancement of image processing and computer technology, machine vision has become capable of performing numerous visual tasks. With the rapid development of cities, the increasing number of cars and traffic volume is increasing, placing increasing pressure on urban roads. Due to disrepair, repeated excavation caused by the construction of additional municipal pipelines, and other factors, some roads have experienced varying degrees of damage, posing a safety hazard to vehicles. Existing road damage detection methods based on machine vision employ road inspection vehicles to capture road surface images, which are then manually analyzed to extract road damage data. However, the above method has the following problems: manual analysis and detection of road surface photos requires the naked eye to identify tiny cracks in the road surface photos, which is labor-intensive and has a long recognition cycle; in addition, manual recognition of a large number of road surface photos is prone to misjudgment and cannot obtain correct damage detection results, which is not conducive to the subsequent work. Summary of the Invention

[0003] To address the above issues, the present invention proposes a machine vision-based pavement damage detection method and system to solve the following problems in the prior art: manual analysis and detection of pavement photos requires the naked eye to identify tiny cracks in the pavement photos, which is labor-intensive and takes a long time to identify; in addition, manual identification of a large number of pavement photos is prone to misjudgment, making it impossible to obtain correct damage detection results, which is not conducive to the development of subsequent work.

[0004] The first technical solution of the embodiment of the present invention is:

[0005] A road damage detection method based on machine vision comprises: obtaining a target detection road surface photo corresponding to a target detection road surface area at a target preset time interval, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; judging whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent area is less than a first target preset photo similarity threshold; if so, obtaining a target detection road surface type corresponding to the target detection road surface area and a target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detection road surface type includes a target asphalt detection road surface and a target cement detection road surface, and the target suspected road surface damage type Including target transverse crack damage type, target vertical crack damage type, target oblique crack damage type and target depression damage type; according to the target detected pavement type and the target suspected pavement damage type, the target suspected damaged area and the target damaged area corresponding to the pre-stored target historical damaged detection pavement photo are compared to determine whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detection pavement photo is a historical pavement photo with a damaged area corresponding to the target detected pavement type; if so, the target suspected damaged area is determined to be a target pavement damaged area, and then the target detected pavement area is determined to be a pavement damaged area.

[0006] The second technical solution of the embodiment of the present invention is:

[0007] A road damage detection system based on machine vision comprises: a road surface photo acquisition module, for acquiring a target detection road surface photo corresponding to a target detection road surface area at a target preset time interval, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; a suspected area judgment module, for judging whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent area is less than a first target preset photo similarity threshold; a damage type acquisition module, for acquiring a target detection road surface type corresponding to the target detection road surface area and a target suspected road surface damage type corresponding to the target suspected damaged area when a target suspected damaged area exists on the target detection road surface photo, wherein the target detection road surface type includes a target asphalt detection road surface and a target cement detection road surface, and the target suspected road surface damage type includes a target transverse crack damage type, target vertical crack damage type, target oblique crack damage type and target depression damage type; a similarity judgment module, used to compare the target suspected damage area with the target damage area corresponding to the pre-stored target historical damage detection road surface photo according to the target detection road surface type and the target suspected road surface damage type, and judge whether the similarity between the target suspected damage area and the target damage area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damage detection road surface photo is a historical road surface photo with a damaged area corresponding to the target detection road surface type; a damage area judgment module, used to judge the target suspected damage area as a target road surface damage area when the similarity between the target suspected damage area and the target damage area is greater than or equal to the second target preset photo similarity threshold, and then judge the target detection road surface area as a road surface damage area.

[0008] The third technical solution of the embodiment of the present invention is:

[0009] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps: obtaining a target detection road surface photo corresponding to a target detection road surface area at a target preset time interval, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; determining whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent area is less than a first target preset photo similarity threshold; if so, obtaining the target detection road surface type corresponding to the target detection road surface area and the target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detection road surface type includes target asphalt detection road surface and Target cement detection pavement, the target suspected pavement damage type includes a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type and a target depression damage type; according to the target detection pavement type and the target suspected pavement damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detection pavement photo, and it is determined whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detection pavement photo is a historical pavement photo with a damaged area corresponding to the target detection pavement type; if so, the target suspected damaged area is determined to be a target pavement damage area, and then the target detection pavement area is determined to be a pavement damage area.

[0010] The fourth technical solution of the embodiment of the present invention is:

[0011] A computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the following steps: obtaining a target detection road surface photo corresponding to a target detection road surface area at target preset time intervals, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; determining whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent area is less than a first target preset photo similarity threshold; if so, obtaining a target detection road surface type corresponding to the target detection road surface area and a target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detection road surface type includes a target asphalt detection road surface and a target cement detection road surface. The target suspected pavement damage type includes a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type and a target depression damage type; according to the target detected pavement type and the target suspected pavement damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detected pavement photo, and it is determined whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detected pavement photo is a historical pavement photo with a damaged area corresponding to the target detected pavement type; if so, the target suspected damaged area is determined to be a target pavement damaged area, and then the target detected pavement area is determined to be a pavement damaged area.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The present invention first obtains a target detection road surface photo corresponding to the target detection road surface area at every target preset time period, and then determines whether there is a target suspected damaged area on the target detection road surface photo. If so, the target detection road surface type corresponding to the target detection road surface area and the target suspected road surface damage type corresponding to the target suspected damaged area are obtained. Finally, according to the target detection road surface type and the target suspected road surface damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detection road surface photo to determine whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold. If so, the target suspected damaged area is determined to be a target road surface damaged area, and then the target detection road surface area is determined to be a road surface damaged area. The present invention can accurately detect the damaged area of ​​the road surface and perform accurate positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] in:

[0016] Figure 1 This is a flowchart of an implementation method of a road damage detection method based on machine vision in one embodiment;

[0017] Figure 2 This is a framework diagram of an implementation scheme of a road damage detection system based on machine vision in one embodiment;

[0018] Figure 3 It is a structural block diagram of an implementation scheme of a computer device in one embodiment. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 , combined with Figure 1 It can be seen that a road damage detection method based on machine vision according to an embodiment of the present invention includes the following steps:

[0021] Step S101: obtaining a target detection road surface photo corresponding to a target detection road surface area at every target preset time period, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database.

[0022] The target preset duration is a preset time length, which can be selected in milliseconds and is not limited thereto. Furthermore, to accurately locate the location of road damage, the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in the target database. Once damage is detected in the target detection road surface area, the damaged area can be accurately located.

[0023] Step S102: determining whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent areas is less than a first target preset similarity threshold.

[0024] The target suspected damaged area is an area on the target detection road surface photograph. The appearance of the target suspected damaged area is significantly different from other areas on the target detection road surface photograph, while the appearance of each portion of the normal, undamaged road surface photograph is not much different. That is, the similarity between the target suspected damaged area and its adjacent areas is less than a first target preset similarity threshold. The first target preset similarity threshold is an empirical threshold and is not limited here.

[0025] Step S103: If there is a target suspected damaged area on the target detection road surface photo, the target detection road surface type corresponding to the target detection road surface area and the target suspected road surface damage type corresponding to the target suspected damaged area are obtained. The target detection road surface type includes a target asphalt detection road surface and a target cement detection road surface. The target suspected road surface damage type includes a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type and a target depression damage type.

[0026] Among them, this step first determines the road surface type corresponding to the target detection road surface area, that is, the target detection road surface type, and then determines the suspected damage type corresponding to the target detection road surface area.

[0027] Among them, the target transverse crack damage type refers to the damage type in which the crack direction of the road surface is perpendicular to the road direction, the target vertical crack damage type refers to the damage type in which the crack direction of the road surface is parallel to the road direction, the target oblique crack damage type refers to the damage type in which the crack direction of the road surface forms an acute angle with the road direction, and the target depression damage type refers to the damage type in which the road surface is depressed by heavy pressure.

[0028] Step S104: Based on the target detected road surface type and the target suspected road surface damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detected road surface photo, and it is determined whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset similarity threshold, wherein the target historical damaged detected road surface photo is a historical road surface photo with a damaged area corresponding to the target detected road surface type.

[0029] The second target preset similarity threshold is an empirical threshold and is not limited here.

[0030] The target historical damaged road surface detection photograph is a historical road surface photograph of a damaged area corresponding to the target detected road surface type. That is, the detected road surface type corresponding to the target historical damaged road surface detection photograph and the detected road surface type corresponding to the target detected road surface area are the same type, for example, both are asphalt roads or both are cement roads. Appearance photographs (including color and shape) of damaged areas of the same road surface type often have similar characteristics, i.e., they have commonalities. Therefore, the similarity between the target suspected damaged area and the target damaged area can be used to determine whether the target suspected damaged area is a true damaged area.

[0031] Step S105: If the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset similarity threshold, the target suspected damaged area is determined to be a target road surface damaged area, and then the target detected road surface area is determined to be a road surface damaged area.

[0032] In this embodiment, optionally, determining whether there is a target suspected damaged area on the target detection road surface photo includes:

[0033] First, the target detection road surface photo is preprocessed to obtain a target detection road surface grayscale photo, and the geographic coordinate data of the target detection road surface grayscale photo corresponds to the geographic coordinate data of the target detection road surface.

[0034] In the target detection road surface grayscale photo, the target detection road surface grayscale photo can be divided into different areas according to different grayscale values. If the grayscale values ​​of two areas differ greatly, it means that the two areas are quite different.

[0035] Second, determine whether there is a target abnormal grayscale area in the grayscale photo of the target detection road surface, the grayscale value of the target abnormal grayscale area is different from the grayscale value of its adjacent area, and the grayscale difference between the target abnormal grayscale area and its adjacent area is greater than the target preset grayscale difference, wherein all grayscale values ​​in the target abnormal grayscale area are the same value, or the grayscale difference between the maximum grayscale value and the minimum grayscale value in the target abnormal grayscale area is less than the second target preset grayscale difference, and the second target preset grayscale difference is less than the first target preset grayscale difference.

[0036] The first target preset grayscale difference and the second target preset grayscale difference are both empirical thresholds, which are not limited here.

[0037] For example, there is an abnormal grayscale area in the middle of the target abnormal grayscale area with all grayscale values ​​being the same (or the grayscale difference between the maximum grayscale value and the minimum grayscale value is less than the second target preset grayscale difference). The grayscale value of this abnormal grayscale area is assumed to be abnormal grayscale, and the grayscale values ​​of other areas adjacent to this abnormal grayscale area are different from this abnormal grayscale, and the grayscale difference is also large.

[0038] Third, if there is a target abnormal grayscale area in the target detection road grayscale photo, it is determined whether the target abnormal grayscale area is a target irregular shape grayscale area, wherein the edge contour of the target irregular shape grayscale area is an irregular shape.

[0039] Among them, the damage to the road surface is generally cracking or denting. The grayscale value of the cracked or dented area is usually different from the grayscale value of other undamaged areas, and the shape of the crack is often irregular, that is, the edge contour of the target irregular shape grayscale area is irregular.

[0040] Fourth, if the target abnormal grayscale region is a target irregular-shaped grayscale region, it is determined whether the target irregular-shaped grayscale region is a water accumulation region.

[0041] Among them, the waterlogged area appears different from other parts in the photo because there is water on the road surface, which may cause detection errors. Therefore, it is necessary to determine whether the target irregular shape grayscale area is a waterlogged area.

[0042] Fifth, if the target irregular-shaped grayscale region is not a water accumulation region, it is determined whether the target irregular-shaped grayscale region is an irregular region formed by accumulation of foreign matter on the road surface.

[0043] Among them, the accumulation of foreign matter on the road surface can also cause errors in road damage detection. The accumulated foreign matter on the road surface occupies a certain area of ​​the detected road surface, and its grayscale value is often the same. Moreover, the grayscale value of the road surface area where the foreign matter is accumulated is often different from the grayscale value of other places on the detected road surface. Therefore, it is necessary to determine whether the target irregular shape grayscale area is an irregular area formed by the accumulation of foreign matter on the road surface.

[0044] Wherein, whether the target irregular-shaped grayscale area is an irregular area formed by accumulation of foreign matter on the road surface can be determined by manual judgment or by photo comparison.

[0045] Sixth, if the target irregular shape grayscale area is not an irregular area formed by accumulation of foreign matter on the road surface, it is determined that the target suspected damaged area exists on the target detection road surface photo.

[0046] In this embodiment, optionally, determining whether the target irregular-shaped grayscale area is a water accumulation area includes:

[0047] First, determine whether the area corresponding to the target irregular-shaped grayscale region gradually decreases within a preset time period.

[0048] The preset time period can be several minutes, more than ten minutes, or other time periods, and is not limited thereto. Because accumulated water evaporates over time, the accumulated water area will become smaller and smaller. Therefore, as long as the area corresponding to the target irregularly shaped grayscale region gradually decreases within the preset time period, it is considered to be an accumulated water area.

[0049] Second, if the area corresponding to the target irregular-shaped grayscale region gradually decreases within a preset time period, it is determined that the target irregular-shaped grayscale region is a water accumulation area.

[0050] In this embodiment, optionally, the step of comparing the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph based on the target detected road surface type and the target suspected road surface damage type, and determining whether a similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, includes:

[0051] First, according to the target detected road surface type and the target suspected road surface damage type, obtain the pre-stored target historical damaged road surface photo and the target damaged area corresponding to the target historical damaged road surface photo.

[0052] The road surface type corresponding to the pre-stored target historical damaged road surface detection photo is the target detected road surface type, and the road surface damage type corresponding to the pre-stored target historical damaged road surface detection photo is the target suspected road surface damage type. Under the same road surface type and road surface damage type, the target damaged area corresponding to the target historical damaged road surface detection photo and the target suspected damaged area corresponding to the target suspected damaged area are highly comparable.

[0053] Second, the target suspected damaged area color corresponding to the target suspected damaged area is compared with the target damaged area color corresponding to the target damaged area to determine whether the similarity between the target suspected damaged area color and the target damaged area color is greater than or equal to the target preset color similarity threshold.

[0054] The target preset color similarity threshold is an empirical threshold and is not limited here. The color of the target suspected damaged area and the color of the target damaged area are both the original photo colors rather than grayscale colors.

[0055] Third, if the similarity between the color of the target suspected damaged area and the color of the target damaged area is greater than or equal to the target preset color similarity threshold, it is determined that the similarity between the target suspected damaged area and the target damaged area is greater than or equal to the second target preset photo similarity threshold.

[0056] In this embodiment, optionally, the step of comparing the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph based on the target detected road surface type and the target suspected road surface damage type, and determining whether a similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, includes:

[0057] First, according to the target detected road surface type and the target suspected road surface damage type, obtain the pre-stored target historical damaged road surface photo and the target damaged area corresponding to the target historical damaged road surface photo.

[0058] The road surface type corresponding to the pre-stored target historical damaged road surface detection photo is the target detected road surface type, and the road surface damage type corresponding to the pre-stored target historical damaged road surface detection photo is the target suspected road surface damage type. Under the same road surface type and road surface damage type, the target damaged area corresponding to the target historical damaged road surface detection photo and the target suspected damaged area corresponding to the target suspected damaged area are highly comparable.

[0059] Second, compare the target suspected damaged area grayscale value corresponding to the target suspected damaged area with the target damaged area grayscale value corresponding to the target damaged area to determine whether the target difference absolute value between the target suspected damaged area grayscale value and the target damaged area grayscale value is less than the target preset difference absolute value.

[0060] The target preset absolute difference value is an empirical threshold and is not limited herein. When the target absolute difference value between the grayscale value of the target suspected damaged area and the grayscale value of the target damaged area is less than the target preset absolute difference value, it indicates that the grayscale value of the target suspected damaged area and the grayscale value of the target damaged area are substantially the same, and it can be determined that the similarity between the target suspected damaged area and the target damaged area is greater than or equal to the second target preset photo similarity threshold.

[0061] Third, if the target absolute value of the difference between the grayscale value of the target suspected damaged area and the grayscale value of the target damaged area is less than the target preset absolute value of the difference, it is determined that the similarity between the target suspected damaged area and the target damaged area is greater than or equal to the second target preset photo similarity threshold.

[0062] In this embodiment, optionally, determining whether there is a target suspected damaged area on the target detection road surface photo includes:

[0063] First, a historical photo of the target road surface under normal detection is obtained, where the historical photo of the target road surface under normal detection is a non-damaged historical photo corresponding to the target road surface area under detection.

[0064] Among them, the shooting environment brightness of the target historical normal detection road surface photo and the shooting environment brightness of the target detection road surface photo are the same or have a small difference, that is, the target historical normal detection road surface photo and the target detection road surface photo are both taken under the same or similar light brightness.

[0065] Second, the target detection road surface photo is compared with the target historical normal detection road surface photo to determine whether the similarity between the target detection road surface photo and the target historical normal detection road surface photo is less than a third target preset similarity threshold.

[0066] The third target preset similarity threshold is an empirical threshold and is not limited here.

[0067] Third, if the similarity between the target detection road surface photo and the target historical normal detection road surface photo is less than a third target preset similarity threshold, it is determined that the target suspected damaged area exists on the target detection road surface photo.

[0068] In this embodiment, optionally, determining whether there is a target suspected damaged area on the target detection road surface photo includes:

[0069] First, a target historical damaged detection road surface photo is obtained, where the target historical damaged detection road surface photo is a damaged historical photo corresponding to the target detection road surface area.

[0070] Among them, the shooting environment brightness of the target historical damage detection road surface photo and the shooting environment brightness of the target detection road surface photo are the same or have a small difference, that is, the target historical damage detection road surface photo and the target detection road surface photo are both taken under the same or similar light brightness.

[0071] Second, the target detection road surface photo is compared with the target historical damage detection road surface photo to determine whether the similarity between the target detection road surface photo and the target historical damage detection road surface photo is greater than or equal to a fourth target preset similarity threshold.

[0072] The fourth target preset similarity threshold is an empirical threshold and is not limited here.

[0073] Third, if the similarity between the target detection road surface photo and the target historical damage detection road surface photo is greater than or equal to a fourth target preset similarity threshold, it is determined that the target suspected damage area exists on the target detection road surface photo.

[0074] See also Figure 2 , combined with Figure 2 It can be seen that a road damage detection system 100 based on machine vision according to an embodiment of the present invention includes:

[0075] A road surface photo acquisition module 10 is configured to acquire a target detection road surface photo corresponding to a target detection road surface area at a target preset time interval, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database;

[0076] A suspected area determination module 20 is configured to determine whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent areas is less than a first target preset photo similarity threshold;

[0077] a damage type acquisition module 30 for acquiring, when a target suspected damaged area exists in the target detected road surface photograph, a target detected road surface type corresponding to the target detected road surface area and a target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detected road surface types include a target asphalt detected road surface and a target cement detected road surface, and the target suspected road surface damage types include a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type, and a target depression damage type;

[0078] a similarity determination module 40 for comparing the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph based on the target detected road surface type and the target suspected road surface damage type, and determining whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detected road surface photograph is a historical road surface photograph corresponding to the target detected road surface type and having a damaged area;

[0079] The damaged area determination module 50 is configured to determine the target suspected damaged area as a target road surface damaged area, and further determine the target detected road surface area as a road surface damaged area, when the similarity between the target suspected damaged area and the target damaged area is greater than or equal to the second target preset photo similarity threshold.

[0080] Figure 3 FIG1 shows an internal structure diagram of a computer device in an embodiment. The computer device can be a terminal or a server. Figure 3As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the above-mentioned road damage detection method based on machine vision. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the above-mentioned road damage detection method based on machine vision. It will be understood by those skilled in the art that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0081] In another embodiment, a computer device is proposed, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps: obtaining a target detection road surface photo corresponding to a target detection road surface area at target preset time intervals, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; determining whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent area is less than a first target preset photo similarity threshold; if so, obtaining a target detection road surface type corresponding to the target detection road surface area and a target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detection road surface type includes a target asphalt The target suspected pavement damage type includes a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type and a target depression damage type; according to the target detection pavement type and the target suspected pavement damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detection pavement photo, and it is determined whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detection pavement photo is a historical pavement photo with a damaged area corresponding to the target detection pavement type; if so, the target suspected damaged area is determined to be a target pavement damaged area, and then the target detection pavement area is determined to be a pavement damaged area.

[0082] In another embodiment, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, the processor performs the following steps: obtaining a target detection road surface photo corresponding to the target detection road surface area at every target preset time period, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; judging whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent area is less than a first target preset photo similarity threshold; if so, obtaining the target detection road surface type corresponding to the target detection road surface area and the target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detection road surface type includes target asphalt detection road surface and target suspected damaged road surface. The target cement detection road surface is characterized in that the target suspected road surface damage type includes a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type and a target depression damage type; according to the target detection road surface type and the target suspected road surface damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detection road surface photo, and it is determined whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detection road surface photo is a historical road surface photo with a damaged area corresponding to the target detection road surface type; if so, the target suspected damaged area is determined to be a target road surface damage area, and then the target detection road surface area is determined to be a road surface damage area.

[0083] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0084] The embodiment of the present invention first obtains a target detection road surface photo corresponding to the target detection road surface area at every target preset time period, and then determines whether there is a target suspected damaged area on the target detection road surface photo. If so, the target detection road surface type corresponding to the target detection road surface area and the target suspected road surface damage type corresponding to the target suspected damaged area are obtained. Finally, according to the target detection road surface type and the target suspected road surface damage type, the target suspected damaged area is compared with the target damaged area corresponding to the pre-stored target historical damaged detection road surface photo to determine whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold. If so, the target suspected damaged area is determined to be a target road surface damaged area, and then the target detection road surface area is determined to be a road surface damaged area. The present invention can accurately detect the damaged area of ​​the road surface and perform accurate positioning.

[0085] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A road damage detection method based on machine vision, characterized in that: include: Obtaining a target detection road surface photo corresponding to the target detection road surface area at every target preset time period, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in the target database; Determining whether there is a target suspected damaged area on the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent areas is less than a first target preset similarity threshold; If so, obtain the target detected pavement type corresponding to the target detected pavement area and the target suspected pavement damage type corresponding to the target suspected damage area, wherein the target detected pavement type includes a target asphalt detected pavement and a target cement detected pavement, and the target suspected pavement damage type includes a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type, and a target depression damage type; According to the target detected road surface type and the target suspected road surface damage type, the target suspected damaged area is compared with the target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph, and it is determined whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset similarity threshold, wherein the target historical damaged detected road surface photograph is a historical road surface photograph corresponding to the target detected road surface type and having a damaged area; wherein the detected road surface type corresponding to the target historical damaged detected road surface photograph and the detected road surface type corresponding to the target detected road surface area are the same type; If yes, the target suspected damaged area is determined as a target road surface damaged area, and the target detected road surface area is further determined as a road surface damaged area; The determining whether there is a suspected target damaged area on the target detection road surface photo includes: Preprocessing the target detection road surface photo to obtain a target detection road surface grayscale photo, wherein the geographic coordinate data of the target detection road surface grayscale photo corresponds to the geographic coordinate data of the target detection road surface; Determine whether there is a target abnormal grayscale area in the grayscale photograph of the target detection road surface, the grayscale value of the target abnormal grayscale area is different from the grayscale value of its adjacent area, and the grayscale difference between the target abnormal grayscale area and the adjacent area is greater than a first target preset grayscale difference, wherein all grayscale values ​​in the target abnormal grayscale area are the same value, or the grayscale difference between the maximum grayscale value and the minimum grayscale value in the target abnormal grayscale area is less than a second target preset grayscale difference, and the second target preset grayscale difference is less than the first target preset grayscale difference; If so, determining whether the target abnormal grayscale region is a target irregular-shaped grayscale region, wherein the edge contour of the target irregular-shaped grayscale region is an irregular shape; If yes, determining whether the target irregular shape grayscale area is a water accumulation area; If not, determining whether the target irregular shape grayscale area is an irregular area formed by accumulation of foreign matter on the road surface; If not, determining that the target suspected damaged area exists on the target detection road surface photo; The step of determining whether the target irregular-shaped grayscale area is a water accumulation area includes: Determining whether the area corresponding to the target irregular-shaped grayscale region gradually decreases within a preset time period; If so, it is determined that the target irregular-shaped grayscale area is a water accumulation area.

2. The machine vision-based road damage detection method according to claim 1, characterized in that: The step of comparing the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph based on the target detected road surface type and the target suspected road surface damage type, and determining whether a similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, includes: According to the target detected road surface type and the target suspected road surface damage type, obtaining a pre-stored target historical damaged road surface photo and the target damaged area corresponding to the target historical damaged road surface photo; Comparing the target suspected damaged area color corresponding to the target suspected damaged area with the target damaged area color corresponding to the target damaged area, and determining whether the similarity between the target suspected damaged area color and the target damaged area color is greater than or equal to a target preset color similarity threshold; If so, it is determined that the similarity between the target suspected damaged area and the target damaged area is greater than or equal to the second target preset photo similarity threshold.

3. The machine vision-based road damage detection method according to claim 1, characterized in that: The step of comparing the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph based on the target detected road surface type and the target suspected road surface damage type, and determining whether a similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, includes: According to the target detected road surface type and the target suspected road surface damage type, obtaining a pre-stored target historical damaged road surface photo and the target damaged area corresponding to the target historical damaged road surface photo; Comparing the grayscale value of the target suspected damaged area corresponding to the target suspected damaged area with the grayscale value of the target damaged area corresponding to the target damaged area, and determining whether a target absolute value of the grayscale value of the target suspected damaged area and the grayscale value of the target damaged area is less than a target preset absolute value of the difference; If so, it is determined that the similarity between the target suspected damaged area and the target damaged area is greater than or equal to the second target preset photo similarity threshold.

4. The machine vision-based road damage detection method according to claim 1, characterized in that: The determining whether there is a target suspected damaged area on the target detection road surface photo includes: Acquire a historical normal detection road surface photo of the target, where the historical normal detection road surface photo of the target is a non-damaged historical photo corresponding to the target detection road surface area; Comparing the target detection road surface photo with the target historical normal detection road surface photo, and determining whether the similarity between the target detection road surface photo and the target historical normal detection road surface photo is less than a third target preset similarity threshold; If so, it is determined that there is a suspected damaged area of ​​the target on the target detection road surface photo.

5. The machine vision-based road damage detection method according to claim 1, characterized in that: The determining whether there is a target suspected damaged area on the target detection road surface photo includes: Acquire a target historical damaged road surface detection photo, where the target historical damaged road surface detection photo is a damaged historical photo corresponding to the target detection road surface area; Comparing the target detection road surface photo with the target historical damage detection road surface photo, and determining whether the similarity between the target detection road surface photo and the target historical damage detection road surface photo is greater than or equal to a fourth target preset similarity threshold; If so, it is determined that there is a suspected damaged area of ​​the target on the target detection road surface photo.

6. A road damage detection system based on machine vision, characterized in that: include: A road surface photo acquisition module, configured to acquire a target detection road surface photo corresponding to a target detection road surface area at a target preset time interval, wherein the target detection road surface geographic coordinate data corresponding to the target detection road surface area is pre-stored in a target database; a suspected area determination module, configured to determine whether a target suspected damaged area exists in the target detection road surface photo, wherein the similarity between the target suspected damaged area and its adjacent areas is less than a first target preset photo similarity threshold; a damage type acquisition module, configured to, when a target suspected damaged area exists on the target detected road surface photograph, acquire a target detected road surface type corresponding to the target detected road surface area and a target suspected road surface damage type corresponding to the target suspected damaged area, wherein the target detected road surface types include a target asphalt detected road surface and a target cement detected road surface, and the target suspected road surface damage types include a target transverse crack damage type, a target vertical crack damage type, a target oblique crack damage type, and a target depression damage type; a similarity determination module, configured to compare the target suspected damaged area with a target damaged area corresponding to a pre-stored target historical damaged detected road surface photograph based on the target detected road surface type and the target suspected road surface damage type, and determine whether the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a second target preset photo similarity threshold, wherein the target historical damaged detected road surface photograph is a historical road surface photograph corresponding to the target detected road surface type and having a damaged area; wherein the detected road surface type corresponding to the target historical damaged detected road surface photograph and the detected road surface type corresponding to the target detected road surface area are the same type; a damaged area determination module, configured to determine the target suspected damaged area as a target road surface damaged area, and further determine the target detected road surface area as a road surface damaged area, when the similarity between the target suspected damaged area and the target damaged area is greater than or equal to a preset similarity threshold of the second target photo; The determining whether there is a suspected target damaged area on the target detection road surface photo includes: Preprocessing the target detection road surface photo to obtain a target detection road surface grayscale photo, wherein the geographic coordinate data of the target detection road surface grayscale photo corresponds to the geographic coordinate data of the target detection road surface; Determine whether there is a target abnormal grayscale area in the grayscale photograph of the target detection road surface, the grayscale value of the target abnormal grayscale area is different from the grayscale value of its adjacent area, and the grayscale difference between the target abnormal grayscale area and the adjacent area is greater than a first target preset grayscale difference, wherein all grayscale values ​​in the target abnormal grayscale area are the same value, or the grayscale difference between the maximum grayscale value and the minimum grayscale value in the target abnormal grayscale area is less than a second target preset grayscale difference, and the second target preset grayscale difference is less than the first target preset grayscale difference; If so, determining whether the target abnormal grayscale region is a target irregular-shaped grayscale region, wherein the edge contour of the target irregular-shaped grayscale region is an irregular shape; If yes, determining whether the target irregular shape grayscale area is a water accumulation area; If not, determining whether the target irregular shape grayscale area is an irregular area formed by accumulation of foreign matter on the road surface; If not, determining that the target suspected damaged area exists on the target detection road surface photo; The step of determining whether the target irregular-shaped grayscale area is a water accumulation area includes: Determining whether the area corresponding to the target irregular-shaped grayscale region gradually decreases within a preset time period; If so, it is determined that the target irregular-shaped grayscale area is a water accumulation area.

7. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the processor is caused to execute the road damage detection method based on machine vision according to any one of claims 1 to 5.

8. A computer device, characterized in that: The system comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the road damage detection method based on machine vision according to any one of claims 1 to 5.

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